جست‌وجوی استاد و پژوهشگر

Zhonghang Xiaمشاهده پروفایل

استاد

• Bioinformatics
• Data Mining
• Machine Learning
• +۲ مورد دیگر

Zhonghang Xia is a Professor in the Department of Computer Science at Western Kentucky University. His primary affiliations include the WKU COHH building (office 4141). His research focuses on Bioinformatics, Data Mining, Machine Learning, Multimedia Computing, and Combinatorial Optimization. He teaches advanced courses such as Machine Learning, Data Mining, Artificial Intelligence, and Thesis Research/Writing. His work emphasizes algorithm development for bioinformatics applications, kernel methods, and multimedia systems optimization. Research interests span peptide identification using machine learning, collaborative filtering prediction, and automation frameworks. His recent work addresses challenges in proteomics data analysis and distributed media systems. Advising focuses on distributed service systems and cloud computing infrastructure. He has contributed to over 20 peer-reviewed publications since 2005, with notable contributions in BMC Bioinformatics , IEEE Transactions , and Journal of Proteome Research . Teaching responsibilities include graduate and undergraduate courses in computer science fundamentals and specialized topics such as research methods and thesis guidance. His lab activities involve interdisciplinary projects combining machine learning with biological data analysis and network optimization.

• Data Mining
• Database Systems
• Temporal Databases
• +۴ مورد دیگر

John F. Roddick is a Professor affiliated with Flinders University in South Australia. His research focuses on data mining, database systems, and temporal databases, with significant contributions to association rule mining, schema evolution, and privacy-preserving techniques. He has collaborated extensively with researchers like Shu-Chuan Chu and Jeng-Shyang Pan, producing over 138 publications across journals and conferences. Key contributions include work on schema versioning, temporal vacuuming in databases, and algorithms for wireless sensor networks. His research extends to image processing, biometrics, and swarm intelligence, with notable applications in traffic prediction and secure communication systems. He has edited conference proceedings and contributed to encyclopedic entries on database systems and data warehousing. Roddick's work often bridges theoretical foundations with practical applications, emphasizing interdisciplinary approaches to data management challenges. His publications span venues such as IEEE Transactions on Knowledge and Data Engineering, Data & Knowledge Engineering, and the Journal of Network and Intelligence.

Ernest Tenienteمشاهده پروفایل

استاد

• Software Engineering
• Database Systems
• Conceptual Modeling
• +۴ مورد دیگر

Ernest Teniente is a full professor at the Universitat Politècnica de Catalunya — BarcelonaTech and director of the inLab FIB . He specializes in software engineering, databases, and IoT systems, with a focus on conceptual modeling, data integration, and automated reasoning. His research spans 20 years, yielding over 60 peer-reviewed publications and leading roles in national/international projects like SUDOQU and DAGIA. He has supervised numerous students across BSc, MSc, and PhD levels, contributing to projects such as an AI-based police patrol tool and semantic IoT infrastructure monitoring. Teaching experience includes undergraduate/postgraduate courses on software analysis, requirements engineering, and advanced databases. He has collaborated with institutions like Mossos d’Esquadra and Barcelona City Council on projects like the Artificial Intelligence-assisted police patrol manager and LinDaFIX for social welfare data analysis. His work bridges academia and industry, emphasizing practical applications in transportation optimization (SORT project) and cybersecurity. Research Highlights : Conceptual modeling and automated reasoning for database schemas IoT semantic integration and critical infrastructure monitoring Artifact-centric business process modeling in UML Projects : SUDOQU (data quality via ontologies) DAGIA (threat detection in ICT infrastructures) REMEDiAL (ontology-based software development) He regularly serves on program committees for top conferences and has coordinated projects funded by Spain’s Ministry of Science. His labs and teams, notably inLab FIB, drive innovation in AI applications, DevOps benchmarking, and IoT ecosystems.

• Data Integration
• Database Systems
• Museum Studies
• +۵ مورد دیگر

Professor Alex Poulovassilis is a distinguished academic and College Fellow at Birkbeck, University of London, where she has held numerous senior roles since 1999. As Emeritus Professor of Computer Science, she has pioneered interdisciplinary research at the intersection of computer science, social sciences, and cultural studies. Her work focuses on data integration, museum analytics, biomedical informatics, and gender equity in STEM. She co-founded the London Knowledge Lab (2003–2015) and Birkbeck Knowledge Lab, fostering collaboration between disciplines. Education: BA Mathematics (Cambridge), MSc/PhD Computer Science (Birkbeck). Her doctoral work on FDL (Functional Database Language) revolutionized functional data models. She has held postdocs at UCL/King’s College London and returned to Birkbeck in 1999 as a 175th Anniversary Readership holder. Research interests span data management systems, museum sector analysis (Mapping Museums project), and educational technologies. Notable projects include: Decentralized web search frameworks (ESPRESSO) Museum resilience during the pandemic (Museums in the Pandemic) GENDER INNOVATION: Promoting female academic entrepreneurship Leadership roles include Pro-Vice Master for Research, Head of Computer Science, and Athena SWAN committee member, advocating for gender equality. She co-edited influential books on data management and gender in innovation. Awards: College Fellowship (Birkbeck), contributor to Athena SWAN Bronze Awards. Continues mentoring PhD students and advancing interdisciplinary research.

• Data-driven web technologies
• Information visualization
• Interaction design
• +۴ مورد دیگر

Dr Alan Cannon is a Research Fellow at Edinburgh Napier University's School of Computing , affiliated with the Centre for Algorithms, Visualisation and Evolving Systems . His work spans data-driven web technologies, information visualization, interaction design, and software evaluation. Education: PhD: Interactive Visualisation Tools for Supporting Taxonomists Working Practice (2006) Alan specializes in creating visualization systems for biological and taxonomic data through projects like MaTSE and TaxVis . His recent work focuses on mobile platforms for safety engineering inspections and data-driven web services for academic and commercial applications. His publications demonstrate expertise in bioinformatics visualization, ontology-driven systems, and mobile application development across multiple domains including taxonomy, property management, and safety engineering. Research Grants: Data Driven Analysis of PEMFT Benefits (2022, £5,000) Solicitor Data Management for TC Young (2018, £5,000) Managing Archaeological Projects (2017, £5,000) Follow-on development for Plansafe Ltd. (2015-2016, £35,837) TrueBuild App Development (2014-2015, £5,000) Scottish Forestry Project (2016-2017, £28,492) Alan has extensive experience in academic-industry collaboration, commercialization activities, and developing visualization tools for complex data sets across biological sciences, engineering, and database systems.

Vasilis Efthymiouمشاهده پروفایل

استادیار

• Big Data
• Database Systems
• Entity Resolution
• +۷ مورد دیگر

Vasilis Efthymiou is an Assistant Professor at the Department of Informatics and Telematics at Harokopio University of Athens (HUA), Greece. His research focuses on Big Data database systems, entity resolution, knowledge graphs, and machine learning applications. He has held postdoctoral positions at IBM Research and FORTH-ICS, and contributed to initiatives like the SemTab challenges and TaDA workshops. Education: Diploma in Computer Science (2010), University of Crete. Masters in Information Systems and Bioinformatics (2012), University of Crete. PhD in Entity Resolution in the Web of Data (2017), University of Crete. Research Interests: Efthymiou’s work emphasizes scalable entity resolution techniques, fairness-aware AI systems, and knowledge graph integration. He explores applications in cybersecurity, natural language interfaces, and privacy-preserving data analytics. Recent projects include automated entity resolution pipelines (e.g., TREATS) and hybrid graph neural networks for entity alignment (HybEA). Publications: His 60+ papers span venues like VLDB, SIGMOD, and AAAI. He co-authored two books and holds four US patents in data systems. Notable works include surveys on end-to-end entity resolution and fairness-aware entity resolution tools. Grants & Labs: He co-organized international workshops (e.g., TaDA) and contributed to benchmarking systems like SemTab. His team develops tools like FairER and pyJedAI, emphasizing open-source solutions for data integration challenges.

• Database Systems
• Data Engineering
• Data Governance
• +۴ مورد دیگر

Sergi Nadal Francesch is a Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the College of Informatics of Barcelona (FIB) and the Department of Service and Information Systems Engineering . He leads research within the inSSIDE and DTIM groups, focusing on database technologies, data governance, and knowledge graph applications. Research areas: Database Systems, Data Engineering, Knowledge Graphs, Data Governance, and Automated Data Lifecycle Management Email: sergi.nadal@upc.edu His recent work explores automated data pipelines , scalable data discovery , and AI-driven medical diagnostics . He contributes to knowledge graph lifecycle development and graph-driven federated data systems , with projects funded by Horizon Europe and Spanish national programs. Scientific Awards : UPC Award for Quality in University Teaching (2021), Best Doctoral Thesis in SISTEDES (2021), EDBT 2025 Best Demo Paper

Yannis Velegrakisمشاهده پروفایل

استاد

• Big Data Management & Analytics
• Knowledge Discovery
• Graph Management
• +۱۵ مورد دیگر

Yannis Velegrakis is a Professor in the Department of Information and Computing Sciences at Utrecht University, where he holds the chair of Very Large Data Management. He leads the Data Intensive Systems research group and is the program leader for the Master’s in Data Science . He is also a part-time faculty member at the University of Trento and a Principal Investigator at the Archimedes AI and Data Science hub, Athena Research Center . Education: PhD in Computer Science, University of Toronto MSc in Computer Science, University of Crete BSc in Computer Science, University of Crete His research focuses on Big Data Management & Analytics , Knowledge Discovery , Graph Management , Data Integration , and Data Quality . His work combines theoretical rigor with practical system development, emphasizing human-centered approaches to data exploration and analysis. He has pioneered methods in example-based search, entity resolution, and dynamic graph analysis. His recent publications reflect a strong trend in knowledge graph exploration , data quality assessment , and dynamic network mining . These works often integrate machine learning with database systems to support interactive and intelligent data discovery. His research bridges foundational data management with applied AI, particularly in the context of real-world data challenges. Scientific Leadership and Awards: PC Chair, ICDE 2024 General Chair, VLDB 2013 PC Chair, EDBT 2021 He has advised numerous researchers and contributed to major projects in data integration and semantic search. His secondary activities include visiting positions at IBM Almaden , AT&T Research Labs , UC Santa Cruz , and University of Paris-Saclay . He has secured funding through collaborative research initiatives and has served on program committees of top international conferences. He leads the Data Intensive Systems group, which develops innovative tools for managing and exploring large-scale, heterogeneous datasets. The group emphasizes systems that support intuitive, example-driven interaction with complex data, aligning with the principles of human-centered AI.

K. Psarakisمشاهده پروفایل

پژوهشگر

• Cloud Computing
• Stream Processing
• Distributed Systems
• +۸ مورد دیگر

K. Psarakis is a researcher in the field of Data-Intensive Systems, focusing on cloud computing, stream processing, and distributed dataflows. Their work bridges theoretical and practical challenges in cloud-native applications and scalable systems. Institution: Affiliated with Data-Intensive Systems Research Focus: Cloud transaction management, autoscaling, geospatial data platforms, and fault tolerance Research Trends Recent publications like Styx and CheckMate highlight innovations in transactional stateful functions and checkpointing protocols. Psarakis also contributes to geospatial data federation (e.g., Topio ) and schema matching techniques ( Valentine ). Collaborations Collaborates with researchers such as G. C. Christodoulou, M. Fragkoulis, and A. Katsifodimos on projects involving open-source platforms and cloud-native systems.

• Database Systems
• Distributed Computing
• Machine Learning
• +۳ مورد دیگر

Carsten Binnig is a researcher affiliated with TU Darmstadt, Germany, focusing on database systems, distributed computing, and machine learning integration. His work spans high-performance data management, query optimization, and hardware-software co-design for modern networks. He holds a PhD from Heidelberg University (2008) and has contributed extensively to conferences like SIGMOD, VLDB, and CIDR. His research emphasizes scalable OLTP/OLAP systems, secure cloud DBMSs, and leveraging GPUs/accelerators for database operations. Notable projects include GOLAP (GPU-accelerated OLAP), ScaleStore (storage engine using NVMe/RDMA), and DBMS Annihilator (workload generator). He co-leads initiatives on testing database systems (DBTest workshop) and benchmarks for shared databases.

Magnus Madsenمشاهده پروفایل

دانشیار

• Programming Languages
• Compilers
• Type Systems
• +۴ مورد دیگر

Magnus Madsen is an Associate Professor at the Department of Computer Science, Aarhus University. He specializes in programming language design, compilers, and type and effect systems, and is the lead developer of the Flix programming language. His research focuses on advancing static analysis techniques and declarative language constructs for effectful and data-driven programming. Research Interests : - Programming language design - Type systems and effect systems - Static program analysis - Datalog and declarative programming - Compiler optimization and implementation Awards & Grants : - 2023: Sapere Aude Grant (Independent Research Fund Denmark) - 2022: Dahl-Nygaard (Junior) Prize (ECOOP) - 2022: STEM Grant (Stibo Foundation) - 2021: Amazon Research Award (collaboration with Jaco van de Pol) - 2020: DFF Project One (Independent Research Fund Denmark) Advising & Collaboration : - Supervises four current PhD students (listed above). - Active in academic service: PC member for ECOOP, OOPSLA, PLDI, and other conferences. - Collaborates with industry (e.g., Google, Systematic) and academia on tool development and language research. Labs & Projects : - Core contributor to the Flix programming language and the CASA (Center for Advanced Software Analysis) initiative. - Involved in interdisciplinary projects combining declarative programming with domain-specific applications.

• Data Management
• Database Systems
• Data Discovery
• +۶ مورد دیگر

Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.

• Distributed Stream Processing
• Data Stream Analytics
• Database Optimization
• +۳ مورد دیگر

Dr. Boris Novikov is a prominent Russian computer scientist affiliated with St Petersburg University and the Higher School of Economics . His research focuses on distributed stream processing, temporal databases, and XML query optimization. Key collaborators include Artem Trofimov , Elena Mikhailova , and Anna Yarygina . Research Areas : Distributed Systems (stream processing, substream management) Database Optimization (vertical partitioning, XML caching) Temporal Data Management Machine Learning (concept drift detection) His recent work on distributed stream processing (2023-2022) explores substream bounding, deterministic models, and resource allocation strategies. Earlier contributions (2012-2008) focused on XML query performance, similarity indexing, and access control frameworks. Technical Trends : Event time alignment in distributed architectures Dynamic residual projection for cybersecurity Bi-objective optimization in database systems Speculative execution with conflict resolution Incremental Lagrangian dual solutions Matrix clustering for partitioning problems

• Database Systems
• Query Processing
• Data Stream Management
• +۵ مورد دیگر

Mirek Riedewald is a Professor in the Department of Computer Science at Northeastern University's College of Engineering. With a prolific publication record spanning from 1999 to 2025, he has established himself as a leading researcher in database systems, particularly in query processing, join algorithms, and exploratory search. His work bridges theoretical database foundations with practical applications in data visualization and data lake management. Dr. Riedewald's research focuses on innovative approaches to database query processing, with recent work emphasizing ranked enumeration algorithms, relational diagram visualization, and data lake disambiguation. His publications in top-tier venues like SIGMOD, VLDB, and ICDE demonstrate his significant contributions to the field. He has developed novel techniques for efficient computation of quantiles over joins, any-k algorithms for ranked enumeration, and relationship-based semantic table union search through his SANTOS system. His recent publications reveal a growing interest in the intersection of database systems with fairness considerations, as seen in his work on fixing top-k rankings that lack individual fairness. He also explores the visualization of complex database queries through relational diagrams, making query understanding more accessible. His research has evolved from traditional database topics like data cubes and stream processing to more contemporary challenges in large-scale data management and analysis. Dr. Riedewald has maintained a strong collaborative network, with frequent co-authors including Wolfgang Gatterbauer (31 papers), Johannes Gehrke (23 papers), and Nikolaos Tziavelis (15 papers). His work demonstrates both theoretical depth and practical relevance, addressing real-world challenges in data management systems. His research group has made significant contributions to exploratory search in databases through the ExploreDB workshop series, which he co-organized for multiple years. He has also contributed to important reference works, including chapters in the Encyclopedia of Database Systems on topics like event and pattern detection over streams and database techniques for scientific simulations.

Michael Cochezمشاهده پروفایل

استادیار

• Machine Learning
• Knowledge Graphs
• Graph Neural Networks
• +۷ مورد دیگر

Dr. Michael Cochez is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, with a secondary appointment in Artificial Intelligence. His research focuses on knowledge graph embeddings, graph neural networks, and neuro-symbolic systems. He has published over 70 works and contributed to datasets like KGloVe and Inductive WN18RR. Research Interests: Machine Learning, Knowledge Representation, Graph Theory Applications, Explainable AI, and Bioinformatics Integration. His work bridges theoretical advancements with practical applications in industry and healthcare. Recent Trends in Publications: Emphasis on scalable knowledge graph systems, causal reasoning in economic forecasting, and neuro-symbolic frameworks for complex queries. Active in organizing workshops on DL4KG and industry knowledge graph scaling. Awards: None listed. Grants: Not specified. Advising: No students listed but contributes to courses like Deep Learning and Machine Learning for Graphs. Labs/Teams: Involved in projects like Graph-Massivizer (sustainable data center modeling) and Graph-Scrutinizer (massive analytics tools). Ancillary Activity: Consultancy in Abcoude since 2022.